import pandas as pd
import numpy as np
import plotly.offline as pyo
import plotly.graph_objs as go
used_bikes_prize_in_india_data_csv = pd.read_csv("Used_Bikes_Prize_in_India_Data.csv")
used_bikes_prize_in_india_data_csv
| bike_name | price | city | kms_driven | owner | age | power | brand | |
|---|---|---|---|---|---|---|---|---|
| 0 | TVS Star City Plus Dual Tone 110cc | 35000 | Ahmedabad | 17654 | First Owner | 3 | 110 | TVS |
| 1 | Royal Enfield Classic 350cc | 119900 | Delhi | 11000 | First Owner | 4 | 350 | Royal Enfield |
| 2 | Triumph Daytona 675R | 600000 | Delhi | 110 | First Owner | 8 | 675 | Triumph |
| 3 | TVS Apache RTR 180cc | 65000 | Bangalore | 16329 | First Owner | 4 | 180 | TVS |
| 4 | Yamaha FZ S V 2.0 150cc-Ltd. Edition | 80000 | Bangalore | 10000 | First Owner | 3 | 150 | Yamaha |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 32643 | Hero Passion Pro 100cc | 39000 | Delhi | 22000 | First Owner | 4 | 100 | Hero |
| 32644 | TVS Apache RTR 180cc | 30000 | Karnal | 6639 | First Owner | 9 | 180 | TVS |
| 32645 | Bajaj Avenger Street 220 | 60000 | Delhi | 20373 | First Owner | 6 | 220 | Bajaj |
| 32646 | Hero Super Splendor 125cc | 15600 | Jaipur | 84186 | First Owner | 16 | 125 | Hero |
| 32647 | Bajaj Pulsar 150cc | 22000 | Pune | 60857 | First Owner | 13 | 150 | Bajaj |
32648 rows × 8 columns
used_bikes_prize_in_india_data_csv.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 32648 entries, 0 to 32647 Data columns (total 8 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 bike_name 32648 non-null object 1 price 32648 non-null int64 2 city 32648 non-null object 3 kms_driven 32648 non-null int64 4 owner 32648 non-null object 5 age 32648 non-null int64 6 power 32648 non-null int64 7 brand 32648 non-null object dtypes: int64(4), object(4) memory usage: 2.0+ MB
used_bikes_prize_in_india_data_csv.describe()
| price | kms_driven | age | power | |
|---|---|---|---|---|
| count | 3.264800e+04 | 32648.000000 | 32648.000000 | 32648.000000 |
| mean | 6.829542e+04 | 26344.625184 | 8.048211 | 213.511302 |
| std | 9.071860e+04 | 22208.527695 | 4.031700 | 134.428868 |
| min | 4.400000e+03 | 1.000000 | 1.000000 | 100.000000 |
| 25% | 2.500000e+04 | 12000.000000 | 5.000000 | 150.000000 |
| 50% | 4.300000e+04 | 20373.000000 | 7.000000 | 150.000000 |
| 75% | 8.000000e+04 | 35000.000000 | 10.000000 | 220.000000 |
| max | 1.900000e+06 | 750000.000000 | 63.000000 | 1800.000000 |
used_bikes_prize_in_india_data_csv.columns
Index(['bike_name', 'price', 'city', 'kms_driven', 'owner', 'age', 'power',
'brand'],
dtype='object')
data = [go.Scatter(x = used_bikes_prize_in_india_data_csv["price"],
y = used_bikes_prize_in_india_data_csv["power"],
text = used_bikes_prize_in_india_data_csv["bike_name"],
mode = "markers",
marker = dict(size = used_bikes_prize_in_india_data_csv["age"],
color = used_bikes_prize_in_india_data_csv["kms_driven"],
showscale = True))]
layout = go.Layout(title = "Bubble Chart of mpg DataSet",
xaxis = dict(title = 'Horsepower'),
yaxis = dict(title = 'Price of Bike'),
hovermode='closest')
fig = go.Figure(data, layout)
pyo.iplot(fig)
pyo.plot(fig,
filename = "tutorial_11 (Bubble Charts Exercise).html",
image_width=1600,
image_height=900,)
'tutorial_11 (Bubble Charts Exercise).html'